Papers Spectral Super-Resolution
“Spectral Super-Resolution” 태그가 달린 논문 18편 · 필터 해제
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
This paper addresses the problem of reconstructing a high-resolution hyperspectral image from a low-resolution multispectral observation. While spatial super-resolution and spectral super-resolution have been extensively…
Spectral ReconstructionSpectral Super-ResolutionSuper-ResolutionFrom Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction
Current hyperspectral image (HSI) reconstruction methods primarily rely on image-level approaches, which are time-consuming to form abundant high-quality HSIs through imagers. In contrast, spectrometers offer a more effi…
Image ReconstructionMambaSpectral Super-ResolutionSuper-ResolutionCompensation based Dictionary Transfer for Similar Multispectral Image Spectral Super-resolution
Utilizing a spectral dictionary learned from a couple of similar-scene multi- and hyperspectral image, it is possible to reconstruct a desired hyperspectral image only with one single multispectral image. However, the di…
Spectral Super-ResolutionSuper-ResolutionTransformer-Driven Inverse Problem Transform for Fast Blind Hyperspectral Image Dehazing
Hyperspectral dehazing (HyDHZ) has become a crucial signal processing technology to facilitate the subsequent identification and classification tasks, as the airborne visible/infrared imaging spectrometer (AVIRIS) data p…
Image DehazingSpectral Super-ResolutionSuper-ResolutionLearning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence
Spectral super-resolution that aims to recover hyperspectral image (HSI) from easily obtainable RGB image has drawn increasing interest in the field of computational photography. The crucial aspect of spectral super-reso…
Spectral Super-ResolutionSuper-ResolutionSpectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction
Coded Aperture Snapshot Spectral Imaging (CASSI) reconstruction aims to recover the 3D spatial-spectral signal from 2D measurement. Existing methods for reconstructing Hyperspectral Image (HSI) typically involve learning…
Image ReconstructionSpectral Super-ResolutionSuper-ResolutionSSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
Existing digital sensors capture images at fixed spatial and spectral resolutions (e.g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning models. Neural Implicit Funct…
Spectral Super-ResolutionSuper-ResolutionFrequency Estimation Using Complex-Valued Shifted Window Transformer
Estimating closely spaced frequency components of a signal is a fundamental problem in statistical signal processing. In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, re…
Image Super-ResolutionSpectral Super-ResolutionSuper-ResolutionPyramid Dual Domain Injection Network for Pan-sharpening
Pan-sharpening, a panchromatic image guided low-spatial-resolution multi-spectral super-resolution task, aims to reconstruct the missing high-frequency information of high-resolution multi-spectral counterpart. Altho…
Spectral Super-ResolutionSuper-ResolutionSeparation-Free Spectral Super-Resolution via Convex Optimization
Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these convex optimization methods however is t…
MiscellaneousSpectral Super-ResolutionSuper-ResolutionMST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI). These…
Image RestorationSpectral ReconstructionSpectral Super-ResolutionHPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to t…
RelationRelation NetworkSpectral Super-ResolutionSuper-ResolutionSpectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution
Hyperspectral images are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high spatial resolution (HR) hyperspectral images from HR multispectral images. Traditional SSR methods…
Spectral Super-ResolutionSuper-ResolutionPixel-aware Deep Function-mixture Network for Spectral Super-Resolution
Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from a given RGB image. Recently, a promising direction for SSR is to learn a complicated mapping function from the RGB image to the HSI coun…
Spectral Super-ResolutionSuper-ResolutionAccurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observat…
Spectral ReconstructionSpectral Super-ResolutionSuper-ResolutionAn efficient CNN for spectral reconstruction from RGB images
Recently, the example-based single image spectral reconstruction from RGB images task, aka, spectral super-resolution was approached by means of deep learning by Galliani et al. The proposed very deep convolutional neura…
Image Super-ResolutionSpectral ReconstructionSpectral Super-ResolutionSuper-ResolutionAerial Spectral Super-Resolution using Conditional Adversarial Networks
Inferring spectral signatures from ground based natural images has acquired a lot of interest in applied deep learning. In contrast to the spectra of ground based images, aerial spectral images have low spatial resolutio…
Spectral Super-ResolutionSuper-ResolutionLearned Spectral Super-Resolution
We describe a novel method for blind, single-image spectral super-resolution. While conventional super-resolution aims to increase the spatial resolution of an input image, our goal is to spectrally enhance the input, i.…
Spectral Super-ResolutionSuper-Resolution